【摘要】：間歇性能源出力預(yù)測(cè)和儲(chǔ)能配置可有效減弱由于間歇性能源并網(wǎng)發(fā)電給電網(wǎng)的運(yùn)行和管理帶來的負(fù)面影響。本文圍繞間歇性能源的出力預(yù)測(cè)以及儲(chǔ)能規(guī)劃開展了研究，主要研究成果如下： 1)針對(duì)風(fēng)電出力預(yù)測(cè)，提出了一種基于改進(jìn)經(jīng)驗(yàn)?zāi)B(tài)分解法和遺傳神經(jīng)網(wǎng)絡(luò)的風(fēng)速預(yù)測(cè)組合模型和算法。通過與GA-BPNN模型、EMD與GA-BPNN組合模型的預(yù)測(cè)結(jié)果對(duì)比，表明所提組合模型和算法預(yù)測(cè)精度較高，且對(duì)超短期預(yù)測(cè)(10min)和短期預(yù)測(cè)(1hour)均能適用。 2)針對(duì)光伏出力預(yù)測(cè)，提出了一種基于相似日的對(duì)太陽輻照量進(jìn)行逐時(shí)預(yù)測(cè)的EEMD和GA-BP組合模型和算法，還提出了一種基于相似日的對(duì)光伏出力進(jìn)行直接預(yù)測(cè)的灰色神經(jīng)網(wǎng)絡(luò)組合預(yù)測(cè)模型和算法。算例結(jié)果表明，所提出的兩種組合算法預(yù)測(cè)精度較高，具有潛在的應(yīng)用價(jià)值。 3)分別提出了既定網(wǎng)架的含風(fēng)電場(chǎng)輸電網(wǎng)的儲(chǔ)能規(guī)劃模型和算法，以及儲(chǔ)能電站與含風(fēng)電場(chǎng)輸電網(wǎng)的協(xié)調(diào)規(guī)劃模型和算法，通過算例測(cè)試了各模型的合理性及方法的有效性，比較并分析了儲(chǔ)能系統(tǒng)與含有風(fēng)電場(chǎng)輸電網(wǎng)規(guī)劃不同方案的優(yōu)劣，可為未來含間歇式能源電網(wǎng)的規(guī)劃發(fā)展提供理論參考。 4)提出了考慮高滲透率光伏的配電網(wǎng)中儲(chǔ)能系統(tǒng)優(yōu)化配置模型和算法。以接入儲(chǔ)能系統(tǒng)后產(chǎn)生的總費(fèi)用的凈現(xiàn)值為目標(biāo)函數(shù)，滿足儲(chǔ)能分時(shí)控制策略約束以及配電網(wǎng)約束的條件下，提出了改進(jìn)的自適應(yīng)粒子群(APSO)算法來求解多類型儲(chǔ)能在含高滲透率光伏配電網(wǎng)中優(yōu)化配置問題。并對(duì)不同儲(chǔ)能技術(shù)接入配電網(wǎng)的經(jīng)濟(jì)性能和節(jié)點(diǎn)電壓變化情況進(jìn)行了比較分析。最后，通過算例結(jié)果驗(yàn)證了所建模型的合理性和所提方法的有效性。 5)利用基于蒙特卡洛模擬的統(tǒng)計(jì)方法從電動(dòng)汽車是否向電網(wǎng)輸送電能和是否受電價(jià)控制兩個(gè)方向出發(fā)建立了3種情境模型，仿真并分析了不同區(qū)域電動(dòng)汽車負(fù)荷情境模型和不同規(guī)模的電動(dòng)汽車對(duì)電網(wǎng)的影響。同時(shí)，提出滿足系統(tǒng)和用戶用電滿意度的雙目標(biāo)的大規(guī)模電動(dòng)汽車充電管理需求響應(yīng)控制策略，并探討電動(dòng)汽車作為儲(chǔ)能的一種形式實(shí)現(xiàn)系統(tǒng)輔助服務(wù)的調(diào)節(jié)能力，為間歇性能源提供輔助服務(wù)做準(zhǔn)備。
[Abstract]:Intermittent energy output prediction and energy storage allocation can effectively reduce the negative effects of intermittent energy generation on the operation and management of power grid. The main research results of this paper are as follows: 1) aiming at wind power generation prediction, A combined model and algorithm for wind speed prediction based on improved empirical mode decomposition (EMD) and genetic neural network (GNN) is proposed. Compared with GA-BPNN model, EMD and GA-BPNN combined model, the proposed combination model and algorithm have high prediction accuracy, and can be applied to both ultra-short term prediction (10min) and short term prediction (1hour). 2) aiming at photovoltaic force prediction, a combined model and algorithm of EEMD and GA-BP is proposed to predict solar irradiance time by time based on similar days. A grey neural network combined prediction model and algorithm based on similar days for direct prediction of photovoltaic force is also proposed. The numerical results show that the two combined algorithms have high prediction accuracy and have potential application value. 3) the energy storage planning model and algorithm of the grid with wind farm and the coordinated planning model and algorithm of the energy storage power station and the transmission network with wind farm are put forward respectively. The rationality of each model and the validity of the method are tested by an example. The advantages and disadvantages of different schemes of energy storage system and transmission network planning with wind farm are compared and analyzed, which can provide a theoretical reference for the planning and development of energy grid with intermittent energy in the future. 4) the optimal configuration model and algorithm of energy storage system in distribution network considering high permeability photovoltaic are proposed. Taking the net present value of the total cost generated by access to the energy storage system as the objective function, the time-sharing control strategy constraint and the distribution network constraint are satisfied. An improved adaptive particle swarm optimization (APSO) algorithm is proposed to solve the problem of optimal configuration of multi-type energy storage in photovoltaic distribution networks with high permeability. The economic performance and node voltage change of different energy storage technology connected to distribution network are compared and analyzed. Finally, the rationality of the proposed model and the validity of the proposed method are verified by an example. 5) using the statistical method based on Monte Carlo simulation, three situation models are established from two directions: whether electric vehicles are supplying electric energy to the power grid and whether they are controlled by electricity price. Simulation and analysis of different regional electric vehicle load situation model and different size of electric vehicles on the power grid. At the same time, the demand response control strategy for charge management of large-scale electric vehicles is proposed, which can meet the requirements of both the system and the users' electricity satisfaction, and the regulation ability of the system auxiliary service is discussed as a form of energy storage. Prepare for intermittent energy support services.
【學(xué)位授予單位】：天津大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM715

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